synthetic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from scipy.stats import gamma\n",
    "import numpy as np\n",
    "import orjson as json\n",
    "import time\n",
    "import altair as alt\n",
    "import pandas as pd\n",
    "# alt.renderers.enable('notebook')\n",
    "\n",
    "\n",
    "VM_URL=\"http://localhost:8428/api/v1/import\"\n",
    "\n",
    "\n",
    "def to_metric_json(name, labels, timestamps, values):\n",
    "    return json.dumps({\"metric\": {\"__name__\": name, **labels},\n",
    "                       \"values\": values,\n",
    "                       \"timestamps\": timestamps\n",
    "                       })\n",
    "\n",
    "\n",
    "def send_metric(url, name, labels, timestamps, values):\n",
    "    resp = requests.post(url, data=to_metric_json(name, labels, timestamps, values))\n",
    "    print(f\"HTTP {resp.status_code}\")\n",
    "\n",
    "\n",
    "def gen_timestamps(n, resolution, end=None):\n",
    "    if end is None:\n",
    "        end = time.time()\n",
    "    return np.linspace(end - n, end, int(n/resolution), dtype=np.int64)*1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted\n"
     ]
    }
   ],
   "source": [
    "# Delete metrics\n",
    "def delete_by_query(query):\n",
    "    resp = requests.get(\"http://localhost:8428/api/v1/admin/tsdb/delete_series\", params={\"match[]\": query})\n",
    "    if not resp.ok:\n",
    "        print(resp.text)\n",
    "    else:\n",
    "        print(\"Deleted\")\n",
    "        \n",
    "# delete_by_query('{__name__!=\"\"}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "  #altair-viz-090289f65598445dbfc1f5a97d35da62.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-090289f65598445dbfc1f5a97d35da62.vega-embed details,\n",
       "  #altair-viz-090289f65598445dbfc1f5a97d35da62.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-090289f65598445dbfc1f5a97d35da62\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-090289f65598445dbfc1f5a97d35da62\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-090289f65598445dbfc1f5a97d35da62\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
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4.773309674167614}, {\"time\": 1727369345000, \"Y\": 3.59790170095251}, {\"time\": 1727369346000, \"Y\": 2.0623680488052463}, {\"time\": 1727369347000, \"Y\": 3.1019954622440884}, {\"time\": 1727369348000, \"Y\": 1.3617595404021352}, {\"time\": 1727369349000, \"Y\": 1.3736435356438903}, {\"time\": 1727369350000, \"Y\": 1.360984184176203}, {\"time\": 1727369351000, \"Y\": 2.4071038212101628}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# base signal, high res\n",
    "n=3600\n",
    "resolution=1 # sec\n",
    "noise = gamma.rvs(2.2, size=n)\n",
    "signal = np.sin(np.linspace(0, np.pi*3, n))*2 + noise\n",
    "signal[1023] = 30\n",
    "signal[2034] = 45\n",
    "timestamps = gen_timestamps(n, 1)\n",
    "\n",
    "original_gauge = alt.Chart(pd.DataFrame({\"time\": timestamps, \"Y\": signal})).mark_line(size=1)\\\n",
    "    .encode(x=\"time:T\", y=\"Y\").properties(width=\"container\")\n",
    "original_gauge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-d3dff4d59efa4529aa2094aa901bfd5b.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-d3dff4d59efa4529aa2094aa901bfd5b.vega-embed details,\n",
       "  #altair-viz-d3dff4d59efa4529aa2094aa901bfd5b.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-d3dff4d59efa4529aa2094aa901bfd5b\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-d3dff4d59efa4529aa2094aa901bfd5b\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-d3dff4d59efa4529aa2094aa901bfd5b\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
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       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"layer\": [{\"data\": {\"name\": \"data-9ffb38f87dfb0a297cb5e5176ff17841\"}, \"mark\": {\"type\": \"line\", \"color\": \"blue\"}, \"encoding\": {\"x\": {\"field\": \"time\", \"type\": \"temporal\"}, \"y\": {\"field\": \"gauge\", \"type\": \"quantitative\"}}}, {\"data\": {\"name\": \"data-9ffb38f87dfb0a297cb5e5176ff17841\"}, \"mark\": {\"type\": \"line\", \"color\": \"green\"}, \"encoding\": {\"x\": {\"field\": \"time\", \"type\": \"temporal\"}, \"y\": {\"field\": \"mean\", \"type\": \"quantitative\"}}}, {\"data\": {\"name\": \"data-9ffb38f87dfb0a297cb5e5176ff17841\"}, \"mark\": {\"type\": \"line\", \"color\": \"red\"}, \"encoding\": {\"x\": {\"field\": \"time\", \"type\": \"temporal\"}, \"y\": {\"field\": \"max\", \"type\": \"quantitative\"}}}, {\"data\": {\"name\": \"data-852c2c458dbf437a4db2c52b77ecc11a\"}, \"mark\": {\"type\": \"line\", \"size\": 1}, \"encoding\": {\"x\": {\"field\": 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       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "window_size = 60\n",
    "lowres = np.split(signal, signal.shape[0]/window_size)\n",
    "lowres_timestamps = timestamps[::window_size]\n",
    "lowres_sample = [random.choice(r) for r in lowres]\n",
    "lowres_avg = [r.mean() for r in lowres]\n",
    "lowres_max = [r.max() for r in lowres]\n",
    "\n",
    "c_base = alt.Chart(pd.DataFrame({\"time\": lowres_timestamps, \"mean\": lowres_avg, \"gauge\": lowres_sample, \"max\": lowres_max})).encode(x=\"time:T\")\n",
    "alt.layer(c_base.mark_line(color=\"blue\").encode(y=\"gauge\"),\n",
    "          c_base.mark_line(color=\"green\").encode(y=\"mean\"),\n",
    "          c_base.mark_line(color=\"red\").encode(y=\"max\"),\n",
    "          original_gauge\n",
    "          ).properties(width=\"container\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted\n",
      "HTTP 204\n",
      "HTTP 204\n",
      "HTTP 204\n",
      "HTTP 204\n"
     ]
    }
   ],
   "source": [
    "# %%script echo skipping \n",
    "# base signal, high res\n",
    "# delete_by_query('{_s_name__=~\"demo_low_res.*\"}')\n",
    "\n",
    "n=24*3600\n",
    "resolution=1 # sec\n",
    "noise = gamma.rvs(2.2, size=n)\n",
    "signal = np.sin(np.linspace(0, np.pi*3, n))*2 + noise\n",
    "peaks_idx = np.random.choice(np.linspace(0, signal.shape[0], signal.shape[0], dtype=\"int64\"), size=10)\n",
    "signal[peaks_idx] = [30 + np.random.randint(5, 20)]\n",
    "now = time.time()\n",
    "timestamps = np.linspace(now - n*resolution, now, n, dtype=np.int64)*1000\n",
    "lowres_window = 10\n",
    "signal[:-1], signal[1:]\n",
    "counter = np.cumsum(signal)[::lowres_window]\n",
    "lowres_timestamps = timestamps[::lowres_window]\n",
    "lowres = np.split(signal, signal.shape[0]/lowres_window)\n",
    "lowres_sample = [float(np.random.choice(r)) for r in lowres]\n",
    "lowres_avg = [float(r.mean()) for r in lowres]\n",
    "\n",
    "send_metric(VM_URL, \"demo_high_res_gauge\", {\"host\": \"demo\", \"res\": \"1sec\"}, timestamps.tolist(), signal.tolist())\n",
    "send_metric(VM_URL, \"demo_low_res_counter\", {\"host\": \"demo\", \"res\": f\"{lowres_window}sec\"}, lowres_timestamps.tolist(), counter.tolist())\n",
    "send_metric(VM_URL, \"demo_low_res_gauge\", {\"host\": \"demo\", \"res\": f\"{lowres_window}s\"}, lowres_timestamps.tolist(), lowres_sample)\n",
    "send_metric(VM_URL, \"demo_low_res_gauge_mean\", {\"host\": \"demo\", \"res\": f\"{lowres_window}s\"}, lowres_timestamps.tolist(), lowres_avg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTTP 204\n"
     ]
    }
   ],
   "source": [
    "n=24*3600\n",
    "window = 30\n",
    "points = int(n/window)\n",
    "timestamps = gen_timestamps(n, window)\n",
    "signal = (np.sin(np.linspace(0, np.pi*24, points))+1)*2 + gamma.rvs(3, size=points)*0.5\n",
    "issue_started = np.random.randint(0, int(points/2))\n",
    "signal[issue_started:issue_started+20] += np.sin(np.linspace(0, np.pi/2, 20))*10\n",
    "\n",
    "# alt.Chart(pd.DataFrame({\"time\": timestamps, \"signal\": signal}))\\\n",
    "#     .mark_line().encode(x=\"time:T\", y=\"signal\")\\\n",
    "#     .properties(width=\"container\")\n",
    "    \n",
    "send_metric(VM_URL, \"demo_zero_bound_with_issue\", {\"host\": \"demo\", \"res\": \"30s\"}, timestamps.tolist(), signal.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted\n",
      "HTTP 204\n"
     ]
    }
   ],
   "source": [
    "# metric with large peak overshadowing signal (by triggering auto axis bounds)\n",
    "# delete_by_query(\"demo_large_peak_shadow\")\n",
    "\n",
    "n=24*3600\n",
    "window = 30\n",
    "points = int(n/window)\n",
    "timestamps = gen_timestamps(n, window)\n",
    "signal = (np.sin(np.linspace(0, np.pi*24, points))+1)*2 + gamma.rvs(3, size=points)*0.5\n",
    "issue_started = np.random.randint(0, int(points/2))\n",
    "signal[issue_started:issue_started+10] += np.sin(np.linspace(0, np.pi/2, 10))*1000\n",
    "\n",
    "\n",
    "\n",
    "# alt.Chart(pd.DataFrame({\"time\": timestamps, \"signal\": signal}))\\\n",
    "#     .mark_line().encode(x=\"time:T\", y=\"signal\")\\\n",
    "#     .properties(width=\"container\")\n",
    "    \n",
    "send_metric(VM_URL, \"demo_large_peak_shadow\", {\"host\": \"demo\", \"res\": \"30s\"}, timestamps.tolist(), signal.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted\n",
      "HTTP 204\n"
     ]
    }
   ],
   "source": [
    "# metric with holes (missing datapoints) masked by line interpolation\n",
    "\n",
    "n=24*3600\n",
    "window = 30\n",
    "points = int(n/window)\n",
    "timestamps = gen_timestamps(n, window)\n",
    "signal = (np.sin(np.linspace(0, np.pi*24, points))+1)*2 + gamma.rvs(3, size=points)*0.5\n",
    "issues = np.random.randint(0, points, size=20)\n",
    "for issue in issues:\n",
    "    signal[issue:issue+5] = np.NaN\n",
    "\n",
    "\n",
    "# alt.Chart(pd.DataFrame({\"time\": timestamps, \"signal\": signal}))\\\n",
    "#     .mark_line().encode(x=\"time:T\", y=\"signal\")\\\n",
    "#     .properties(width=\"container\")\n",
    "    \n",
    "send_metric(VM_URL, \"demo_missing_points\", {\"host\": \"demo\", \"res\": \"30s\"}, timestamps.tolist(), signal.tolist())\n"
   ]
  }
 ],
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